Python for LSTMs, ARIMA, Deep Learning, AI, Support Vector Regression, +More Applied to Time Series Forecasting
What you’ll learn
ETS and Exponential Smoothing Models
Holt’s Linear Trend Model and Holt-Winters
Autoregressive and Moving Average Models (ARIMA)
Seasonal ARIMA (SARIMA), and SARIMAX
The statsmodels Python library
The pmdarima Python library
Machine learning for time series forecasting
Deep learning (ANNs, CNNs, RNNs, and LSTMs) for time series forecasting
Tensorflow 2 for predicting stock prices and returns
Vector autoregression (VAR) and vector moving average (VMA) models (VARMA)
AWS Forecast (Amazon’s time series forecasting service)
FB Prophet (Facebook’s time series library)
Modeling and forecasting financial time series
GARCH (volatility modeling)
Decent Python coding skills
Numpy, Matplotlib, Pandas, and Scipy (I teach this for free! My gift to the community)
Welcome to Time Series Analysis, Forecasting, and Machine Learning in Python.
Time Series Analysis has become an especially important field in recent years.
- With inflation on the rise, many are turning to the stock market and cryptocurrencies in order to ensure their savings do not lose their value.
- COVID-19 has shown us how forecasting is an essential tool for driving public health decisions.
- Businesses are becoming increasingly efficient, forecasting inventory and operational needs ahead of time.
Let me cut to the chase. This is not your average Time Series Analysis course. This course covers modern developments such as deep learning, time series classification (which can drive user insights from smartphone data, or read your thoughts from electrical activity in the brain), and more.
We will cover techniques such as:
- ETS and Exponential Smoothing
- Holt’s Linear Trend Model
- Holt-Winters Model
- ARIMA, SARIMA, SARIMAX, and Auto ARIMA
- ACF and PACF
- Vector Autoregression and Moving Average Models (VAR, VMA, VARMA)
- Machine Learning Models (including Logistic Regression, Support Vector Machines, and Random Forests)
- Deep Learning Models (Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks)
- GRUs and LSTMs for Time Series Forecasting
We will cover applications such as:
- Time series forecasting of sales data
- Time series forecasting of stock prices and stock returns
- Time series classification of smartphone data to predict user behavior
The VIP version of the course will cover even more exciting topics, such as:
- AWS Forecast (Amazon’s state-of-the-art low-code forecasting API)
- GARCH (financial volatility modeling)
- FB Prophet (Facebook’s time series library)
So what are you waiting for? Signup now to get lifetime access, a certificate of completion you can show off on your LinkedIn profile, and the skills to use the latest time series analysis techniques that you cannot learn anywhere else.
Thanks for reading, and I’ll see you in class!
Who this course is for:
- Anyone who loves or wants to learn about time series analysis
- Students and professionals who want to advance their career in finance, time series analysis, or data science
Created by Lazy Programmer Team, Lazy Programmer Inc.
Last updated 7/2021
Size: 4.84 GB